This BBC Click segment highlights the artificial intelligence system developed by CSAIL researchers that can monitor people’s movements through walls. Prof. Dina Katabi explains that the device helps preserve the privacy of those being monitored by separating and encrypting, “any identifiable information from the measurement.”
Fast Company reporter Steven Melendez writes that CSAIL researchers have created a new system that allows a robot to detect human brainwave patterns so it knows when it made a mistake. Melendez explains that, “Teaching robots to understand human nonverbal cues and signals could make them safer and more efficient at working with people.”
In an article for The New York Times, graduate student Joy Buolamwini writes about how AI systems can often reinforce existing racial biases and exclusions. Buolamwini writes that, “Everyday people should support lawmakers, activists and public-interest technologists in demanding transparency, equity and accountability in the use of artificial intelligence that governs our lives.”
MIT researchers have developed a system that allows people to use a combination of brain waves and muscle signals to stop and redirect a robot, writes John Biggs for TechCrunch. “The machine adapts to you, and not the other way around,” explains graduate student Joseph DelPreto.
MIT researchers have developed a system that “lets a person control a robotic arm with brainwaves and subtle hand gestures,” reports Jesus Diaz for Co.Design. According to Prof. Daniela Rus, the goal is “to develop robotic systems that are a more natural and intuitive extension of us.”
A new system developed by MIT researchers analyzes radio signals that bounce off of human bodies to track their movement and posture from behind walls, write Saqib Shah for Fox News. Shah suggests that the system could allow military personal “to ‘see’ hidden enemies by wearing augmented reality headsets.”
By measuring how radio waves bounce off of human bodies, MIT researchers have developed a system that can track movements from behind a wall, writes Andrew Liszewski for Gizmodo. The researchers are working to improve on the current stick figure icons by “generating 3D representations that include subtle and small movements,” writes Liszewski.
Researchers led by Prof. Dina Katabi have developed a system to track people’s movements from behind a wall, writes Kaleigh Rogers of Motherboard. Earlier versions were unable to track precise movements, but the system can now interpret signals bouncing off bodies and “translate it into the movement of 14 different key points on the body, including the head, elbows, and knees.”
CSAIL researchers have created a system that can sense a person’s movements through walls, writes John Biggs for TechCrunch. The system is primarily intended as a healthcare device and could help with “passive monitoring of a subject inside a room without cameras or other intrusions,” and could provide insight into disease progression, Biggs explains.
Fast Company reporter Melissa Locker writes that CSAIL researchers have developed a system that allows wireless devices to sense a person’s movement through walls. Locker explains that the technology was created as a way to help those who are elderly, as it could be used to “monitor diseases like Parkinson’s and multiple sclerosis and provide a better understanding of disease progression.”
In response to a reader’s question about self-driving cars, Mercury News reporter Gary Richards describes new technology in the works by MIT researchers to allow, “driverless cars to change lanes more like human drivers do.”
Visiting Lecturer Irving Wladawsky-Berger writes about Prof. Thomas Malone’s book, Superminds, which examines how machines are becoming increasingly able to complement human intelligence. Wladawsky-Berger writes that Malone shows how, “humans can supply the general intelligence and whatever other skills machines don’t have, and machines can supply the vast information, computational power and other specialized capabilities that people don’t have.”
To prove that the data used to train machine learning algorithms can greatly influence its behavior, MIT researchers input gruesome and violent content into an AI algorithm, writes Benjamin Fearnow for Newsweek. The result is “Norman,” an AI system in which “empathy logic simply failed to turn on,” explains Fearnow.
In this video, BBC Click spotlights VirtualHome, a simulator developed by CSAIL researchers that could be used to teach robots to perform household chores. The researchers hope the system could one day allow for seamless human-robot collaboration by allowing robots to, “cooperate with [humans] in finishing their activity,” explains graduate student Xavier Puig.
HuffPost reporter Thomas Tamblyn writes that MIT researchers developed a new AI system that sees the worst in humanity to illustrate what happens when bias enters the machine learning process. “An AI learns only what it is fed, and if the humans that are feeding it are biased (consciously or not) then the results can be extremely problematic.”